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C^3 

BUREAU OF MINES 
INFORMATION CIRCULAR/1989 




Predicting the Failure of Electric Motors 



By Gerald T. Homce 



UNITED STATES DEPARTMENT OF THE INTERIOR 



Information Circular 921 1 

ii 



Predicting the Failure of Electric Motors 



By Gerald T. Homce 



UNITED STATES DEPARTMENT OF THE INTERIOR 
Donald Paul Hodel, Secretary 

BUREAU OF MINES 
T S Ary, Director 






Library of Congress Cataloging in Publication Data: 



Homce, Gerald T. 






Predicting the failure of electric motors. 






(Bureau of Mines information circular; 9211) 






Includes bibliographical references. 






Supt. of Docs, no.: I 28.27:9211. 






1. Mining machinery— Electric driving— Reliability. 


2. Failure 


ime data analysis. 


I. Title. II. Series: Information circular (United States. Bureau 


of Mines); 9211. 


TN295.U4 [TK4059.M55] 622 s 


[622'.028] 


88-600247 



CONTENTS 

Page 

Abstract 1 

Introduction 2 

Background 2 

Recent failure prediction research 2 

Data collection 3 

Failure prediction research results 3 

General 3 

Methodology 3 

Theoretical analysis of algorithm 4 

System specification 4 

System structure 4 

System operation 5 

System utility 5 

Model development 6 

Cable-connected motor models 6 

Induction motor deterioration model 6 

Computer analysis 7 

Cable deterioration 7 

Motor deterioration 9 

Experimental program 9 

Deteriorating-motor experiments 10 

Destructive testing 11 

Electrically excited vibration 11 

Theoretical study 11 

Laboratory experiments 11 

Practical applications of research results 12 

Computer model use 12 

Off-line failure prediction techniques 13 

Summary 13 

U.S. Navy submarine power system monitoring 14 

General 14 

Submarine data acquisition 14 

Summary 15 

Conclusions 15 



ILLUSTRATIONS 

Page 

1. Cable-connected motor system for Bureau of Mines laboratory test program 3 

2. Laboratory equipment for data collection from cable-connected motor system 3 

3. Functional diagram of condition and performance monitoring system 5 

4. Information flow in failure prediction algorithm 6 

5. Effect of line-to-line cable insulation deterioration on feature complex power 8 

6. Effect of line-to-line cable insulation deterioration on kappa transform values for complex power 8 

7. Illustration of a nonlinear response as deterioration approaches fault level 8 

8. Effect of winding-to-winding insulation deterioration within a motor, on feature current 9 

9. Effect of winding-to-winding insulation deterioration within a motor, on kappa transform values 

for current 9 

10. Effect of winding-to-winding insulation deterioration within a motor, on motor efficiency 10 

11. Effect of motor load on feature current, for a constant level of winding-to-winding 

insulation deterioration 10 

12. Line current values, I a , I b , and 1^ for sudden failure during destructive testing of motor 11 

13. Equipment connections for submarine power system data acquisition 15 



UNIT OF MEASURE ABBREVIATIONS USED IN THIS REPORT 


A ampere 


mA 


milliampere 


hp horsepower 


pet 


percent 


Hz hertz 


rpm 


revolution per minute 


kW kilowatt 







PREDICTING THE FAILURE OF ELECTRIC MOTORS 



By Gerald T. Homce 1 



ABSTRACT 

A system capable of monitoring a mine electrical power system to detect incipient electrical compo- 
nent failure could significantly improve power system safety and availability. The U.S. Bureau of Mines 
funded a contract with The Pennsylvania State University (Perm State) to establish the theoretical and 
technical framework for such a system. This report briefly outlines the contract, reviewing related 
Bureau and Penn State work prior to its award, and describes support work carried out by the Bureau. 
The main focus of the report is on research efforts by Penn State and subsequent results. An existing 
algorithm for incipient-failure detection and classification was studied, and recommendations are made 
to improve its performance. In addition, mathematical models of cable-connected motor systems and 
deteriorating motors were developed and implemented on computers. These models and laboratory 
tests were used to study and document relationships between component deterioration and electrical 
terminal effects. The project was supported by the U.S. Navy and the nature of its interest in the 
application of failure prediction techniques is also included. 



Electrical engineer, Pittsburgh Research Center, U.S. Bureau of Mines, Pittsburgh, PA. 



INTRODUCTION 



Monitoring and control technology is currently being 
applied to many aspects of mining operations. Computer- 
based systems can now aid mine operators in the manage- 
ment of production, health and safety monitoring, process 
and equipment control, and other activities. Another area 
in which such systems can be applied is the monitoring of 
mine electrical power systems for maintenance purposes. 
U.S. Bureau of Mines research, through contract JO338028 
with The Pennsylvania State University (Perm State), 
focused on the development of a system to monitor the 
performance of mine power systems and to allow detection 
of component deterioration in very early stages. 

A system capable of detecting incipient electrical 
component failure would impact mining operations in two 
areas. One benefit would be the implementation of 
predictive maintenance programs to increase equipment 



availability and thereby improve productivity. In addition, 
such a system would enhance personnel safety. 2 Electrical 
system deterioration, detected early, could often be cor- 
rected before a substantial shock or fire hazard developed. 
Furthermore, the need for emergency power system main- 
tenance, which tends to be rushed and substandard, could 
often be avoided. 

This report details the progress of Bureau research in 
the detection of incipient electrical component failure and 
results to date. A brief review of past failure prediction 
research and its contribution as a foundation for present 
work is given. A description of contract JO338028 and an 
overview of laboratory support work by the Bureau are 
presented. The majority of the report covers research 
efforts and results by Perm State. A section is included 
that describes U.S. Navy involvement in this program. 



BACKGROUND 



Research in the detection of incipient electrical 
component failure, conducted from 1974 to 1983, formed 
the foundation for the development of an electrical power 
system monitoring-failure prediction system. The Bureau 
funded a research program at Perm State in 1974 (grant 
GO155003), to develop a continuous monitoring system 
that could predict electrical safety hazards on mine 
electrical power systems. Ultimately, the program failed 
to produce an operational monitoring system; it did, 
however, yield valuable information for subsequent failure 
prediction research. 3 Work continued at Penn State with 
university research funds, and efforts focused on areas that 
were unresolved at the close of the grant. 4 Early progress 
included more clearly defining the problem, creation of a 
system concept, identifying relationships between 



terminal electrical characteristics and failure modes, and 
development of a failure prediction algorithm to serve as 
a basis for the proposed system. The primary results were 
a working algorithm and identification of monitoring 
system requirements. A number of points remained to be 
addressed, however, such as algorithm performance 
problems, further study of failure modes and their 
electrical signatures, and internal failure of induction 
motors. 

At this stage, the Bureau examined the research per- 
formed since 1974, and a decision was made to establish a 
new program to further study the application of failure 
prediction to mining electrical power systems. Conse- 
quently, contract JO338028 was awarded to Penn State in 
August 1983. 



RECENT FAILURE PREDICTION RESEARCH 



This contract specified research that would further 
develop the basis for a power system performance moni- 
toring-failure prediction system. The primary original 
objective was the collection and analysis of power system 
deterioration characteristics, to improve the existing failure 



^Morley, L. A., F. C. Trutt, and J. L. Kohler. Final Report-Evalu- 
ation of Coal-Mine Electrical-System Safety (grant G0155003, PA State 
Univ.). BuMines OFR 160-81, 1981, 202 pp.; NTIS PB 82-139338. 

'Work cited in footnote 2. 



prediction algorithm. Contract modifications extended 
research efforts to include mathematical modeling of 
cable-connected motor systems and induction motors, 
examination of electrically excited stator vibration effects, 
and specification of the proposed monitoring system. 

"Kohler, J. L. A Decision-Theoretic Method for the Classification of 
Incipient-Failure Patterns Which Are Characteristics of Deteriorating 
Mine Power-System Components. Unpublished Ph.D. Thesis, PA 
State Univ., Mar. 1983, 141 pp.; available upon request from J. L. 
Kohler, PA State Univ., University Park, PA. 



DATA COLLECTION 



Under the provisions of contract JO338028, a laboratory 
power system and a data acquisition system were estab- 
lished at the Bureau's Pittsburgh (PA) Research Center, 
as per specifications from Penn State. The arrangement 
was designed to generate data to support analytical work 
for the contract, by sampling voltage and current phasors 
from an operating three-phase cable-connected motor 
system. More specifically, the collected information 
covered areas such as- 
Laboratory motor parameters, 
Data acquisition system characteristics, 

Validation of mathematical power system models, 
and 



3-phase power 



Induction 
motor 



n) (m) (n) 



Motor 

alternator 

set 



5 or 
10 hp 



Fault 

leakage -cur rent 

simulator 



17.5 kW 



Single-phase 
alternator 



Variable load 
bank 



-vw- 
-vw- 



Figure 1.— Cable-connected motor system for Bureau of Mines 
laboratory test program. 



Various modes of power system deterioration for 
evaluation of the prediction algorithm. 

Specified experiments called for a squirrel-cage induc- 
tion motor with deterioration simulated on the incoming 
cable. The motor was directly coupled to a single-phase 
alternator that in turn powered a variable load bank to 
create motor load cycles. Faults external to the motor (on 
the cable) were simulated by a variable load placed across 
various phase and ground combinations. The overall 
layout of the cable-motor system is shown in figure 1. The 
Bureau's data acquisition equipment included signal con- 
ditioning interfaces, a fiber optic signal transmission 
system, and a data collection computer system. Informa- 
tion was digitized, processed, and organized using Bureau 
developed software. Figure 2 shows the general arrange- 
ment of the Bureau's experimental apparatus for failure 
prediction research. 



To motor- 
alternator set 



Fiber optic 
receivers 



Voltage and current 

sensing and 
signal conditioning 




Motor test 
station 



Laboratory 

3-phase 

power source 



Fiber optic 
transmitters 



Laboratory 

data 

acquisition 

computer 

system 




Data files 
storage 



Figure 2.— Laboratory equipment for data collection from cable- 
connected motor system. 



FAILURE PREDICTION RESEARCH RESULTS 



GENERAL 

The original objective of the contract, was refinement 
of the existing failure prediction algorithm through analysis 
of laboratory generated data. Subsequent modifications 
broadened the program scope to include efforts that would 
support this refinement, including extensive computer 
modeling of power systems and motors undergoing dete- 
rioration, examination of electrically excited vibration in 
motors, and application of failure prediction techniques to 



Navy submarine power system maintenance. This section 
will describe the results of contractor work in all these 
areas, with the exception of Navy applications, which are 
discussed in a separate section. 

METHODOLOGY 

Initial work focused on identifying performance anom- 
alies in the existing algorithm and determining the cause 
of these anomalies. Project researchers have determined, 



however, that nearly all anomalies are related to hardware 
sampling and monitoring system sensitivity problems. 5 

A revised sampling scheme eliminated most of the 
hardware-related problems, but difficulties associated with 
monitoring sensitivity required more extensive investi- 
gation. This investigation called for more and different 
power system data, and in response, additional laboratory 
experimentation was carried out. Such an approach, how- 
ever, proved too expensive and time-consuming for the 
volume of data needed; therefore, computer modeling of 
deteriorating power system components was undertaken to 
generate terminal values needed for the work. The devel- 
opment of appropriate models not only freed researchers 
from the limitations of available laboratory equipment, but 
decoupled the analysis from the hardware characteristics 
of any particular measurement system. 

In addition to mathematical modeling of deteriorating 
power systems, other areas added, to the original contract 
scope of work were specification of a proposed perfor- 
mance and condition monitoring system and examination 
of electrically excited motor vibration with respect to 
failure prediction techniques. These modifications not only 
further refined existing failure prediction theory, but also 
generated independent and more immediately useful 
results. Details of results for each area are presented in 
the balance of this section. 

THEORETICAL ANALYSIS OF ALGORITHM 

A significant performance anomaly from past tests of 
the failure prediction algorithm was a large standard 
deviation among samples for identical cases of component 
deterioration. A firm correlation was established between 
this problem and the method used for sampling voltage 
and current phasors. The sampling technique used prior 
to this contract was unable to accurately trigger at a 
predetermined target current level. The associated devia- 
tion in sampling points resulted in unacceptable variations 
in voltage and current values. Without a series of accu- 
rately reproduced points from motor load cycles, the algo- 
rithm had difficulty identifying changes in features (values 
derived from voltage and current values) that were due 
only to power system deterioration. A modified triggering 
system rectified this situation by sampling a range of 
values, up to nine cycles in duration. The data acquisition 
software could then extract from this range the best match 
to the desired target level. 

Sampling trigger techniques are also a probable source 
for another anomaly, asymmetry of algorithm performance 
among different phases. Past results have indicated 
decreasing prediction accuracy for phases A, B, and C, 
respectively, and this may be attributable to sampling 
schemes that monitor phase A for triggering. A solution 
to this is the use of a sampling method that triggers from 

'Pennsylvania State University. Prediction of Incipient Electrical 
Component Failure. Ongoing BuMines contract J0338028; for inf., 
contact J. C. Cawley, TPO, Pittsburgh Research Center, BuMines, 
Pittsburgh, PA. 



some characteristic of motor loading, such as speed, that 
does not rely on one particular phase. A suitable trig- 
gering technique should also be independent of system 
deterioration. 

Early testing of the algorithm also revealed erratic 
results for conductor degradation on a power system. 
Tests using data representing this mode of deterioration 
randomly exhibited either extremely good or extremely 
poor classification accuracy. The electrical characteristics 
of physical conductor degradation were, therefore, 
addressed in a series of tests. These tests proved very dif- 
ficult to complete because of temperature effects, which 
often had more influence on cable resistance than reduc- 
tion in conductor cross-sectional area. Test results, 
however, along with computer simulation of conductor 
degradation, sufficiently defined the deterioration to permit 
decisions regarding further examination of the problem. 

Analysis indicated that in No. 8 AWG cable and larger, 
the incremental resistance change for severing of individual 
strands was beyond the sensitivity of the existing moni- 
toring system. In addition, cable resistance change reacts 
exponentially to point reduction in conductor cross-sec- 
tional area, with an eventual sharp increase in heating 
effect influence and ultimate burn through of the cable. 
These factors give conductor degradation a somewhat all 
or nothing characteristic, and thus it was determined that 
monitoring for such deterioration was beyond the capa- 
bilities of the present failure prediction system. 

SYSTEM SPECIFICATION 

The on-line incipient failure prediction system proposed 
by the contractor is based on a software approach that 
utilizes simple, rugged sensors and microcomputers. 6 With 
one microcomputer servicing multiple motors, and rela- 
tively low hardware costs, motors as small as 5 hp could be 
monitored economically. The difficulties and expense of 
such an approach are primarily in the development of soft- 
ware, which requires research into degradation mecha- 
nisms for electrical components. 

System Structure 

The proposed system monitors power system bus vol- 
tage, using simple voltage division circuits. While present 
work monitors all three phases, future implementations 
may only require one phase voltage value, depending on 
sensitivity requirements. Line currents for each power sys- 
tem component in question are monitored using current 
transformers shunted with resistors to produce an output 
voltage proportional to the primary current. Voltage and 
current signals then undergo analog-to-digital conversion 
for input to a microcomputer. Given the comparative 
techniques employed by the failure prediction algorithm 
software, the resolution of the monitoring system hardware 
is more important than its absolute accuracy. Thus, rela- 
tive accuracy and long-term stability are important factors 

^ork cited in footnote 5. 



in instrumentation selection. Based on results thus far, the 
proposed monitoring system will be capable of detecting 
deterioration levels approximately two orders of magnitude 
lower than those that will typically affect power system 
performance or safety. Figure 3 is a functional diagram of 
the proposed system. 7 

System Operation 

The current state of the failure prediction algorithm 
requires that voltage and current phasors be sampled from 
a reproducible point during motor operation. Most of the 
data generated for this program have used a specific 
current level (from phase A) during motor loading for this 
reproducible point. The 60-Hz components of these 
phasors are then used to calculate a number of features 
for the power system, including system impedance (real 
and imaginary components), complex power, and power 
factor. These, along with the original voltage and current 
phasors, form patterns that are used to evaluate system 
condition. 

The first step is a yes-no check for deterioration based 
on the presence of negative sequence current. If deterio- 
ration is detected, feature values are preprocessed, which 
involves comparing features from the sample under evalu- 
ation to a reference feature set that represents normal 
operation (no deterioration). 

The initial preprocessing step is the use of interphase 
distance (kappa) transforms. Earlier research has demon- 
strated that the actual values of pattern features are too 
variable within the patterns to be useful for classification 
of system deterioration. Use of the kappa transform, how- 
ever, reduces the variability while retaining characteristics 
unique to the pattern and the type of deterioration it 
represents. The kappa transform is, by definition, the 
change over time of the difference between feature values 
for two particular phases. 8 If Xa, Xb, and Xc are feature 
values for phases a, b, and c for some class of deteri- 
oration, and X'a, X'b, and X'c are the reference feature 
values for the same power system, the kappa transforms 
are 

K(l) = (Xa - Xb) - (X'a - X'b), 

K(2) = (Xb - Xc) - (X'b - X'c), 

and K(3) = (Xc - Xa) - (X'c - X'a). 

These values then undergo a statistical level of 
significance test based on feature standard deviations, 
wherein each feature is assigned a +1 (significant 
increase), (no change), or -1 (significant decrease). The 
significance test reduces the effects of power system noise 
when attempting to classify mode and location of 
deterioration, particularly at low levels. The resulting 
collection of mathematically modified features forms a 



Motors 



r^ 



\ /* 



Current 
transformers 

3 -phase bus 



Voltage 
signals- 



\ 



I 



\ 



.11 S2 1 



Current 

transformer 

signals 



Signal 
conditioning 



I 



Ana log- to-digital 
converter 



Microprocessor 



s 



Figure 3.— Functional diagram of condition and performance 
monitoring system. 

pattern in N-dimensional space (where N is the number 
of features). The pattern is mapped into a partitioned 
decision space that can determine the type and location of 
deterioration. Such a partitioned decision space is trained 
using data from motor systems operating under known 
deterioration conditions. 

This has been only a brief description of the failure 
prediction algorithm, but more detail, as well as the 
FORTRAN source code, can be found in the work cited 
in footnote 4. Figure 4 is an information flow diagram for 
the algorithm. 9 

System Utility 

The hardware and software, installed as an on-line 
failure prediction system, would be capable of monitoring 
both power system condition and performance. 10 Perfor- 
mance monitoring focuses on operational characteristics of 
system components and, using the electrical features listed 
earlier in this section, could be used to study system 



Work cited in footnote 5. 
8 Work cited in footnote 5. 



^Vork cited in footnote 5. 
10 Work cited in footnote 5. 



Z Digitized 7 
signals /~ 



Fast fourier 
transform 



Feature 
extraction 



/ Voltage and 7 
/current phasors/ 



"Symmetrical 
components 

"System 
impedance 



Continue 
sampling 



"Complex power 
( and power factor) 

"Voltage and 
current phasors 




£ 



Performance 
evaluation 



Kappa transform and 
significance test 



Z Performance / 
information / 



Feature pattern 
classification 



Report 
generation 



/System-component/ 
/ condition / 



Figure 4.— Information flow in failure prediction algorithm. 



application problems, load characteristics, and efficiency. 
Performance evaluation can in some cases reveal power 
system component problems, but condition monitoring, by 
definition, attempts to accurately detect incipient compo- 
nent deterioration as early as possible and determine its 
type and location. The failure prediction system proposed, 
used as an on-line monitoring system, would be capable of 
detecting- 
Cable insulation deterioration (line-to-line or line- 
to-ground), 

Motor stator turn-to-turn leakage (wye or delta), 

Motor stator to ground leakage, 

Uniform insulation leakage, and 

Shorted connections. 

Although much of the basic research for a failure pre- 
diction system is complete, information from recent work 
must still be incorporated into existing techniques and 
further research conducted before such a system is ready 
for implementation. Areas that will further refine failure 
prediction monitoring include improvement of sensing and 
signal processing hardware, a better understanding of the 
relationships between motor deterioration and electrical 
signatures, modification of detection and classification 



software, and inclusion of mechanical parameters such as 
electrically excited vibration in motors. 

MODEL DEVELOPMENT 

Efforts to refine the failure prediction algorithm created 
a need for an economical method to generate terminal 
values (voltage and current phasors) for deteriorating elec- 
trical components. This led to the theoretical development 
and computer implementation of mathematical models for 
cable-connected motor systems undergoing cable deterio- 
ration and for squirrel-cage induction motors experiencing 
stator insulation failure. 11 With these models, researchers 
created a data base of electrical features with which to 
evaluate patterns associated with incipient failures. 

Cable-Connected Motor Models 

Mathematical modeling of a cable-motor system must 
include the positive and negative sequence impedance pre- 
sented by the induction motor. This information is 
obtained using a per-phase equivalent circuit for an induc- 
tion motor, with parameters taken from manufacturer's 
data or laboratory tests. In addition to motor circuit 
equivalent impedances, other important information for a 
cable-motor system includes cable impedance, fault type, 
fault location, and fault impedance. Applied voltage is 
assumed to be known, and motor speed can be assumed 
or determined by an iterative solution for a given line 
current level. With this information, symmetrical compo- 
nent techniques can be employed to determine voltage and 
current values for specific phases. 

The three general cases modeled using this approach 
were conductor degradation (increased impedance), line- 
to-ground cable leakage, and line-to-line cable leakage. 
The simulation of conductor degradation has had only 
limited use, since early in the program the failure predic- 
tion algorithm was found to be unsuitable for detecting 
this type of deterioration. Line-to-ground and line-to-line 
fault modeling, however, were implemented in computer 
programs and used to evaluate failure conditions. Results 
of their use are discussed in the "Computer Analysis" sec- 
tion, and a more detailed description of their theory and 
use can be obtained from the work cited in footnote 5. 

Induction Motor Deterioration Model 

Initial attempts to model the effects of internal deteri- 
oration on an induction motor used a symmetrical com- 
ponent solution of the motor system equivalent circuit. 
In this circuit analysis, turn-to-turn leakage is represented 
by a reduction in the number of turns in a faulty phase. 
The solution also requires that stator phase windings be 
represented by concentrated full-pitched coils, that deteri- 
oration of insulation has progressed to a zero-resistance 
state, and that the motor is a two-pole machine. These 
assumptions, however, severely limit the utility and 

u Work cited in footnote 5. 



accuracy for evaluation of deterioration involving turn-to- 
turn leakage. 

Given these limitations, a more general analysis 
approach was pursued, resulting in a mathematical model 
able to predict terminal values for an induction motor 
experiencing a wide variety of internal stator faults. The 
overall approach for construction of this model involved 
the following seven steps. 

1. The airgap magnetic flux (including space harmon- 
ics) produced by a single stator coil is theoretically 
evaluated. 

2. The portion of this flux that links a second stator 
coil is then determined. 

3. Using the results of steps 1 and 2, an expression for 
the mutual impedance between an arbitrary pair of stator 
coils is determined. 

4. Considering a winding to be a series connected set 
of coils (these series sets are defined by the specific fault 
situation), the results of step 3 may be summed to give 
expressions for the mutual impedance between stator 
windings. 

5. Similar approaches to steps 1 through 4 are then 
utilized to obtain expressions for self and mutual imped- 
ances relating to stator-rotor, rotor-rotor, and rotor-stator 
interactions. 

6. The effects of leakage impedances are then added to 
the model, and Kirchhoff s voltage law is utilized to obtain 
a set of N-equations having the N-winding currents as 
unknown quantities. 

7. The equations of step 6 are then solved to give the 
winding and line currents, symmetrical components 
current, input power, and effective power factor. Effi- 
ciency is also estimated. 

A detailed theoretical development of this model is 
available from the work cited in footnote 5. 

COMPUTER ANALYSIS 

Models for cable-connected motor systems and deteri- 
orating motors were implemented in FORTRAN programs 
and validated using experimental laboratory data. In gen- 
eral, model predictions and laboratory data agreed well, 
with differences remaining below a few percent. The 
validated models were then applied to the analysis of 
relationships between power system conditions-characteris- 
tics and calculated terminal features. 12 



Cable Deterioration 

The application of the cable-connected motor models 
addressed the following three general topics: 

Feature patterns resulting from cable deterioration 
in a cable-motor system. 

Level of sensitivity required (from a failure 
prediction monitoring system) to detect given levels of 
deterioration. 

Effect of different component types and sizes on 
feature patterns. 

The results from an analysis of cable leakage and its 
effects on specific electrical features are presented in 
graphical form in the work cited in footnote 5, where the 
reaction of numerous features and their respective kappa 
values are graphed for line-to-line and line-to-ground cable 
leakage of varying severity. Figures 5 and 6 are examples 
of these plots, which represent the change in feature values 
and kappa values, respectively, for complex power as line- 
to-line deterioration level increases. Although this infor- 
mation is not directly applicable to failure prediction in the 
form presented, it serves to give a general feel for deterio- 
ration effects on a power system. More importantly 
though, the sum of all such feature reactions is the key to 
the pattern recognition process used in the failure predic- 
tion algorithm. As described earlier in the report, the 
kappa values are more useful for pattern recognition than 
the actual feature values. 

The cable-connected motor system models were also 
used to evaluate system sensitivity; that is, to determine the 
best possible performance from specific monitoring hard- 
ware or select hardware for a desired level of sensitivity. 

In order to study sensitivity, deterioration levels and 
their relation to power systems needed to be better 
defined. Deterioration levels of interest are those that 
would normally go undetected. Practical limits for these 
are levels above which protective devices will operate or 
levels that cause changes noticeable to human operators. 
In the first case, this would commonly be above 125 pet 
normal line current; in the second case, an estimate based 
on practical experience is a negative sequence current 
25 pet or more of normal line current. Past results indi- 
cated that deterioration could be detected well below these 
values, and in the sensitivity analysis they were used as 
upper limits of deterioration. This definition of deterio- 
ration level limits is further supported by a tendency for 
features to be linear at low levels of deterioration, and 
nonlinear as fault levels are approached. Figure 7 is an 
example of this tendency for a feature value and the third 
kappa value of current. 13 



12 Work cited in footnote 5. 



13 Work cited in footnote 5. 



, 64 

L 48 

O o> 
°-§ 32 

Q- Q. 

i o 



1 


1 


i 


i 


i 


i 


1 T 1 

/Phase A 


- 












Phase B 














f Phase C 


1 1 1 1 1 1 1 1 1 


10 


20 


30 


40 


50 


60 


70 80 90 10 



LEAKAGE CURRENT, pet of normal full load current 

Figure 5.— Effect of line-to-line cable insulation deterioration on 
feature complex power. 




0.0049 



0.0098 ' 0.0491 

LEAKAGE CURRENT PER UNIT 



0.0982 



Figure 6.— Effect of line-to-line cable insulation deterioration on 
kappa transform values for complex power. 

In actual sensitivity analyses, kappa values for various 
levels of cable deterioration were examined. Sensitivity 
required to detect a given leakage impedance was deter- 
mined by the resulting change in the kappa value of a 
measured feature. For example, if a 1,250-ohm line-to- 
line leakage impedance causes a line current kappa value 
of 100 mA, then the monitoring system used must have 
resolution capable of detecting 100-mA change. In addi- 
tion, random (uncontrollable) fluctuations in measure- 
ments due to sampling errors, temperature changes, etc., 
must be well below 100 mA. Another consideration when 
dealing with deterioration involving ground is the influence 
of any power system grounding impedance. Since a 
grounding impedance is in series with any line-to-ground 
leakage impedance, it influences the leakage current. The 
influence is minimal when the leakage impedance is one or 
two orders of magnitude larger than the grounding imped- 
ance; when the grounding impedance is high (ground flow 
current limited to <1 A, for example), it acts to mask 
changes in measured features. In the latter situation, mon- 
itoring resolution must, therefore, be better than would be 
necessary on a system with a lower grounding impedance, 
to detect comparable deterioration levels. 

Random fluctuations in a power system and monitoring 
system cause measurement changes that do not relate to 




I0 1 



I0 2 I0 3 I0 4 I0 5 

LEAKAGE IMPEDANCE, Si 



Figure 7.— Illustration of a nonlinear response as deterioration 
approaches fault level. 

load changes or deterioration. These factors, which 
include conductor temperature, air temperature and 
humidity, sampling errors, and supply voltage fluctuations, 
have an increasing influence on failure prediction accuracy 
as changes due to deterioration become smaller. Although 
they are not controllable, most effects can be predicted 
and accounted for, if necessary. A 5-pct supply voltage 
imbalance, for example, causes a small but significant 
change in kappa values. The effects of this imbalance, 
however, can be subtracted from total system imbalances 
to improve detection accuracy, if necessary. 

The third topic analyzed using the cable-connected 
motor models was the effect of different types and sizes of 
power system components on feature kappa values. The 
type and size of cable (assuming correct sizing for load) 
does not affect feature values, since cable impedances are 
typically small compared to leakage impedances. Thus the 
performance of the failure prediction algorithm is 
unaffected by cable type or size. 

The effect of motor type and size, however, is not so 
easily identified or defined. Motor parameters can vary 
drastically, even for machines with identical horsepower 
ratings; consequently, their impedances will also vary 
widely. Since a leakage path is essentially in parallel with 
motor impedance, motor characteristics affect the accuracy 



and sensitivity of failure prediction techniques. Although 
use of per-unit values in analysis reduces the apparent 
variation for dissimilar motors, the effects are still signif- 
icant and become more pronounced as deterioration levels 
increase. Analysis of these variations presented an addi- 
tional problem, since identical leakage paths cannot be 
created on systems with different components. The evalu- 
ation, therefore, was carried out using several different 
criteria for deterioration levels, and results for each were 
compared. 

The analysis involved producing several feature kappa 
values for three different cable-motor systems with similar 
leakage paths. For the systems examined, sensitivity to 
similar deterioration was different for different motors 
by as much as 36 pet, but the overall feature patterns 
remained the same in almost all cases. This suggests that 
while sensitivity criteria may have to be situation specific, 
the incipient failure classification process is independent of 
motor type and size. Additionally, the criteria used for 
defining the level of deterioration had little effect on fea- 
ture variation among the different motor types. 




I 2 3 4 5 6 

LEAKAGE CURRENT PER UNIT 
Figure 8.— Effect of winding-to-winding insulation deterioration 
within a motor, on feature current. 



Motor Deterioration 



Analysis of computer-generated deteriorating motor 
data was less extensive than that for cable-motor systems, 
since the motor model had been available for a shorter 
time and input data were not as readily available. 
Terminal values and the resulting features were produced, 
however, for a motor undergoing turn-to-turn stator dete- 
rioration, both within one winding and between two 
windings (phase to phase). The runs made were not com- 
prehensive and the analysis was not exhaustive, but feature 
reactions to various parameter changes can be shown by 
selected graphs from the work cited in footnote 5. 

Figures 8 and 9 are the feature values and kappa values, 
respectively, of line current for a motor at 75 pet full load, 
and various levels of winding to winding leakage. 
Figure 10 shows motor efficiency for the same situation, as 
deterioration increases. Line current for the same motor 
and leakage path are shown in figure 11, but for a constant 
deterioration level and varying motor load. Additional 
cases examined were the effect on features of changing 
leakage path location (winding to winding) while holding 
load and deterioration level constant, and the behavior 
exhibited by features during turn-to-turn leakage within the 
same winding. 

The information derived from analyses using the cable- 
connected motor models and deteriorating motor model 
is essential to application of the failure prediction 
algorithm. The results better define the effects of 
numerous power system and deterioration parameters on 
terminal electrical features, and will allow more effective 




1 2 3 4 5 6 
LEAKAGE CURRENT PER UNIT 

Figure 9.— Effect of winding-to-winding insulation deterioration 
within a motor, on kappa transform values for current. 

use of these features for incipient failure detection and 
classification. 

EXPERIMENTAL PROGRAM 

The experimental program at Perm State supported 
analytical work by aiding in the development of mathe- 
matical deterioration models, validating the completed 
models, and investigating various application issues. Most 



10 




I 2 3 4 5 6 
LEAKAGE CURRENT PER UNIT 

Figure 10.— Effect of winding-to-winding insulation deterioration 
within a motor, on motor efficiency. 




480 490 500 5I0 520 530 540 550 560 570 580 590 
SPEED, rpm 

Figure 11. -Effect of motor load on feature current, for a 
constant level of winding-to-winding insulation deterioration. 

work involved laboratory simulation of a deteriorating 
induction motor and destructive testing of motors. 14 

Deteriorating-Motor Experiments 

An extensive data collection program was carried out to 
document feature patterns associated with motor deterio- 
ration and to develop a data base of patterns for use in 
development of pattern classification functions. A nonde- 
structive laboratory-simulation approach was used, utilizing 
a Hampden Universal Machine, a deterioration simulator, 
and a data acquisition system. Testing was organized to 
simulate several different fault types at different stator 

14 Work cited in footnote 5. 



locations, in delta- and wye-connected motors. Research- 
ers had direct control over test conditions relating to 
motor-winding connections, deterioration simulation, and 
motor loading. Research requirements were arranged into 
logical test procedures defined by the following system 
parameters: 

Winding connection (delta or wye). 

Deterioration type (phase-to-phase, phase-to-ground, 
or within a single phase). 

Deterioration location (within windings). 

Motor load. 

Deterioration level. 

The resulting experimental procedure had a total of 322 
test cases. The primary result of the tests was a large col- 
lection of electrical feature patterns, but a number of 
general comments can be made regarding the behavior 
exhibited by the deteriorating motor. 

Features that were more sensitive than others to change 
in winding insulation degradation included the following: 

Power factor at no-load conditions. 

Line impedance. 

Magnitude of line currents. 

Zero and negative sequence currents. 

Rotor double-frequency component. 

Power factor exhibits a marked change with leakage 
level increase, but the effect diminishes quickly when the 
motor is loaded. The line currents also display increasing 
imbalance as deterioration increases, but unlike power fac- 
tor, their relative positions remain constant for motor load 
changes. Similarly, zero and negative sequence current 
increase proportionally to deterioration, while remaining 
independent of motor load. 

Although the connection between deterioration level 
and negative sequence current was evident from test 
results, the relationship was not consistent. Additional 
analysis indicated that leakage current and negative 
sequence current are directly related for constant leakage 
path potential. This was then extended to suggest that a 
direct relationship exists between negative sequence cur- 
rent level and power consumed by a leakage path, for cur- 
rents limited only by leakage impedance. Verification of 
this is only preliminary, but such a correlation would allow 
negative sequence information to be used as an indication 
of deterioration severity. Double-frequency rotor currents 
are related to negative sequence stator currents, and are 
also extremely sensitive to system imbalance. They, how- 
ever, can only be monitored on wound-rotor machines. 



11 



Destructive Testing 

Accelerated life cycle testing of induction motors was 
conducted to verify model predictions and laboratory dete- 
rioration simulations. Test results are terminal value fea- 
ture patterns from actual motor failures for comparison 
to simulated or modeled values. The acceleration pro- 
cesses, however, were not quantified, and so no insulation 
life predictions are intended. 

The method chosen for accelerated aging was a combi- 
nation of electrical stressing, thermal stressing, and mois- 
ture exposure. High dc voltage was placed across stator 
windings for electrical stress; thermal stresses were created 
by overloading the motor while restricting ventilation. 
Moisture was introduced by a humidifier and by direct 
spraying. 

Three-phase voltage and current values were monitored 
during testing, with phasors measured and digitized for 
storage at regular intervals, and continuous magnetic 
taping used to ensure recording of unexpected failures. In 
addition, insulation resistance tests were conducted at 
regular intervals for comparison to terminal value feature 
patterns. 

One specific test resulted in a motor failure on the 
208th day of operation. This was a sudden failure which 
produced feature patterns that closely match those for a 
winding-to-winding leakage path simulated on the Hamp- 
den Universal Machine. Although line currents exhibited 
a sharp increase at the point of failure, the test motor 
continued to run after the failure occurred. Line current 
changes at failure are shown in figure 12. 15 

In addition to the correlation between actual and 
simulated failure, the destructive testing program helped 
identify several monitoring implementation problems. The 
foremost of these was random fluctuations in terminal 
values when measured over a long period of time. The 
factors that were most notable during testing were bus 
voltage imbalances and temperature changes, which caused 
impedances to vary. 

Overall, the experimental program successfully sup- 
ported failure prediction theory development and 
mathematical modeling of motor deterioration. Additional 
benefits included verification of laboratory simulations and 
examination of monitoring implementation problems. 

ELECTRICALLY EXCITED VIBRATION 

One aspect of the failure prediction program was a pre- 
liminary investigation of electrically excited vibration in 
deteriorating motors. A theoretical study of electrically 
excited vibration was made to determine the feasibility of 
modeling its relationship to stator deterioration. Such a 
model could be used to develop vibration monitoring tech- 
niques as part of a failure prediction system. Limited 
laboratory experiments were also carried out to observe 
electrical-mechanical interactions for a deteriorating 
motor. 




200 300 400 
TAPE COUNTER 



500 600 



Figure 12.— Line current values, l a , l b , and l c , for sudden failure 
during destructive testing of motor. 



Theoretical Study 

This part of the investigation involved a literature 
search and subsequent review of pertinent information on 
electrically excited stator vibration. Researchers concluded 
that it is feasible to construct an electrically excited stator 
vibration model, and a general theoretical approach for 
such modeling was outlined. An approximate analysis, 
using the general approach outlined, was used to compare 
a normal motor and one undergoing phase-to-phase stator 
deterioration. The differences in the resulting frequency 
spectra were significant and indicated that vibration moni- 
toring may prove useful for incipient failure detection. 

Laboratory Experiments 

Experiments were conducted to differentiate between 
electrically and mechanically induced motor vibration, and 
to identify the electrically induced vibration due specifically 
to motor deterioration. Several motors were fitted with 
vibration transducers, with the outputs monitored on a 
waveform analyzer. Vibration spectra were recorded for 
the motor running with no deterioration, the motor rotat- 
ing immediately after removing power, and the motor 
running under single-phasing conditions. Subtracting the 
vibration present after removing motor power from total 
vibration leaves only electrically excited vibration, which 
can then be used in comparison of deteriorated and non- 
deteriorated cases. For the tests run, two important 
results were noted. The change in vibration spectra due 
solely to electrical imbalance during motor deterioration is 
significant, but this change can be entirely different for 
different motors. 



15 Work cited in footnote 5. 



12 



In summary, this investigation has determined that 
modeling of electrically excited vibration in deteriorating 
induction motors is feasible. Furthermore, results from 
laboratory experiments indicate that to continue research 
in this area, such modeling will likely be necessary because 
of the complex and machine specific relationships between 
electrical deterioration and mechanical vibration. Further 
investigation of this topic could enhance the capabilities of 
incipient failure detection techniques by adding an 
additional parameter with which to recognize motor 
deterioration. 

PRACTICAL APPLICATIONS OF RESEARCH 
RESULTS 

The theoretical and experimental work under this pro- 
gram have thus far been described only as applied to 
development of an on-line automated failure prediction 
system. The concepts and tools resulting from these 
efforts, however, have independent value and may be 
immediately useful to maintenance engineers. The utility 
of these items can be described under two categories: 
(1) use of computer models for evaluation of power system 
component behavior and (2) use of simplified feature 
pattern analysis for manual incipient failure detection. 

Computer Model Use 

Analysis of power system components or branches can 
be augmented by computer simulation of system condi- 
tions. Examples of situations to which models could be 
applied include 

Examination of terminal feature patterns for fre- 
quently encountered component failures, 

Determining possible causes for observed component 
problems, and 

Analysis of effects on performance, for changes in 
component characteristics or application. 

The following are brief descriptions of the computer 
models developed under this program to support failure 
prediction research. 

Cable- connected motor modeling first requires the use 
of a program to determine positive and negative sequence 
impedances. A program named SPEED is used if motor 
line currents are available, and another called MOTOR Z 
is employed if motor speed is known. The balance of 
required input for either program is 

Stator resistance, 

Stator reactance, 

Magnetizing branch resistance, 



Magnetizing branch reactance, 

Rotor resistance, 

Rotor reactance, 

Motor synchronous speed, and 

Phase A to ground voltage. 

Outputs in either case are positive and negative sequence 
motor impedances, which are required input for the cable- 
connected motor system modeling programs. 

The modeling programs and the conditions they 
simulate are 

CASE 1-conductor degradation. 

CASE 2-line-to-ground deterioration. 

CASE 3-line-to-line deterioration. 

Input parameters for the programs are 

Phase A to ground voltage. 

Motor horsepower rating. 

Motor positive sequence impedance. 

Motor negative sequence impedance. 

Cable impedance. 

Leakage (fault) impedance. 

Leakage (fault) position. 

Voltage base. 

Impedance to ground (CASE 2 program only). 

The models compute the voltage and current phasors that 
would exist at the line side of the cable-connected motor 
system in question. From these values, the programs 
derive and output a number of features including current 
(echo), complex power and its components, power factor, 
complex impedance and its components, current symmet- 
rical components, and kappa values for all of these fea- 
tures (requires reference data set). Instructions for use 
of these programs as well as their FORTRAN source code 
are found in the work cited in footnote 5. 

The deteriorating motor model developed under this 
program, MTRMDL, simulates internal stator deterio- 
ration, and so requires extensive motor design and deterio- 
ration description data for input. Input information 



13 



selection requires a basic understanding of electric machin- 
ery as well as the modeling techniques used, and includes 
data describing 

Network connections and impedances for the motor 
and deterioration condition in question, 

Complete physical and electrical characteristics of 
the motor, and 

Motor operating conditions. 

The output of MTRMDL is voltage and current phasors 
at the subject motor's terminals. The work cited in 
footnote 5 contains the FORTRAN source code for 
MTRMDL and instructions for program use, including 
selection of input information. 

Off-Line Failure Prediction Techniques 

Failure prediction theory has not yet reached a point at 
which it can support an on-line fully automated system for 
incipient failure detection. Several aspects of this research, 
however, are sufficiently developed to have some utility 
for manual implementation. Although when considering 
a nonautomated approach, definite guidelines are not 
available to allow quick detection or classification of power 
system deterioration, application of these manual 
evaluation techniques can still provide more information 
on component operating performance and condition than 
is normally possible. To allow use of such performance 
and condition monitoring on an interim basis, a program 
called THREE-PHASE ANALYZER was derived from 
the formal incipient failure detection algorithm. 

The first step of off-line monitoring would be to 
measure and record the necessary values from the system 
under test. Any method used must accurately record the 
voltage and current values while maintaining all phase rela- 
tionships. Selection of a data acquisition system, however, 
raises many of the questions brought forth earlier in this 
report, such as required accuracy and resolution of 
hardware, method of analog-to-digital conversion, sampling 
point reproducibility, sample length, sampling method, 
sampling speed, and random fluctuations in the power and 
monitoring system. Although these factors are important 
for acquisition of accurate and appropriate data, they are 
situation specific and will not be covered here. 

Input to the THREE-PHASE ANALYZER program 
consists of the line-to-neutral voltage and line-current 
phasors monitored at the terminals of the electrical com- 
ponent in question. The program requires input of 
phasors for a reference condition (no deterioration) as 
well, in order to calculate feature value differences 
(between reference and present case) and kappa values. 
Reference data should come from samples at some known 



condition, such as when a motor is new or recently rebuilt. 
Output from the program consists of 

Voltage phasors (echo), 

Current phasors (echo), 

Complex power and its components, 

Power factor, 

Complex impedance and its components, 

Symmetrical components for all the above, and 

Kappa values for all the above. 

Further directions for use and the FORTRAN source code 
listing of THREE-PHASE ANALYZER are in the work 
cited in footnote 5. 

Output from the THREE-PHASE ANALYZER 
contains information (raw feature values for the test data) 
useful for power system component performance evalua- 
tion. Items such as voltage balance and phase relationship 
can be used to check power supply quality, while current 
levels, power consumed, and power factor describe motor 
load level and general efficiency for the application. The 
raw feature values are then subtracted from the reference 
set to yield feature change values; interphase distance 
transforms are applied to create kappa values. Using this 
information, changes in system-component condition can 
be detected. A notable rise in negative sequence current 
for instance (not due to supply voltage imbalance), indi- 
cates some sort of deterioration, and examination of other 
feature changes can better define likely locations and 
modes for the incipient failure. This sort of analysis would 
be most productive if progressive deterioration can be 
identified and documented, from point of first detection to 
actual failure. Such trending information will be essential 
for the eventual extension of incipient failure detection to 
accurate failure prediction. 

Computer modeling and manual failure prediction are 
direct results of efforts to create an automated failure pre- 
diction system. Although their practical applications are 
limited, in appropriate situations they can be useful tools 
for maintenance engineers in the evaluation of power 
system component performance and condition. 

SUMMARY 

Research by Pcnn State under the failure prediction 
program has focused on establishing a theoretical frame- 
work for a feasible incipient failure prediction system. 
Work to refine an existing failure prediction algorithm 
exposed many aspects of electrical deterioration theory 



14 



that required more development. The necessary additional 
analysis involved mathematical modeling of deteriorating 
cable-connected motor systems and induction motors with 
internal deterioration, and the computer implementation 
of the models. Extensive laboratory testing was also con- 
ducted to simulate deterioration conditions. In addition, 
a theoretical and experimental examination of electrically 
excited vibration determined its utility for deterioration 
detection, and the feasibility of mathematically modeling 
its effects. 



Through these activities, researchers identified areas on 
which to focus analysis, implemented computer models 
and experimental programs to carry out the analysis and, 
as a result, defined many relationships between terminal 
electrical features and system component deterioration. 
Additionally, they specified the proposed incipient failure 
detection system, established the significance of electrically 
excited vibration effects, and described immediate applica- 
tions for the interim results of this program. 



U.S. NAVY SUBMARINE POWER SYSTEM MONITORING 



GENERAL 



SUBMARINE DATA ACQUISITION 



The Submarine Monitoring Maintenance Systems Office 
of the U.S. Navy partially funded the failure prediction 
program, under agreement N002485RAAZ001 with the 
Bureau. The U.S. Navy is interested in the application of 
failure prediction techniques to existing submarine power 
system maintenance programs, and specifically requested- 

An examination of electrically excited vibration in 
induction motors, the feasibility of modeling its effects, and 
evaluation of its utility for deterioration detection; 

General specifications for an on-line monitoring 
system, including a definition of its capabilities; and 

Delivery of data collection hardware, analysis 
software, and procedures documentation, for use as an off- 
line performance-condition monitoring system. 

Electrically excited vibration and on-line monitoring 
system specifications were covered in the previous section. 
The last item listed, however, is a deliverable that involves 
measuring voltage and current phasors from a power 
system component, processing these phasors using the 
THREE-PHASE ANALYZER program, and manually 
analyzing the results to evaluate performance and possibly 
detect incipient deterioration. The purpose of such a 
monitoring system is to give Navy engineers an interim 
failure prediction method with which to judge the merits 
and feasibility of expanding their monitoring techniques. 



Use of the THREE-PHASE ANALYZER and manual 
analysis of terminal feature values have been previously 
discussed, but off-line monitoring also requires some 
method of collecting information from a power system on- 
board a nuclear-powered submarine. Voltage and current 
values must be obtained in such a manner that identifica- 
tion, time base, and sequence relationships remain intact. 
In addition, original power system magnitude values must 
be available from reproduction signals. Bureau personnel, 
therefore, researched, designed, and constructed a portable 
data collection system to meet these criteria. 

Bureau engineers visited a nuclear-powered submarine 
in order to attempt data collection from the power system, 
and assess the requirements for an on-board data acquisi- 
tion system. It was determined that beyond functional 
requirements, any system devised must be reasonably 
simple and safe to operate, small and light because of 
physical constraints on-board a submarine, and self- 
contained for convenience. 

Basic components for the system are a data collection- 
storage device, sensors and leads for connection to the 
power system, and an interface unit to link the sensors and 
leads to the recording device. The first two categories 
were filled by commercially available items, while the 
interface required custom design and construction to 
address the unique characteristics and environment of a 
submarine power system. A portable seven-channel FM 
instrumentation tape recorder-reproducer was selected for 
signal recording and storage. Clamp-on-type current 



15 



transformers are used to sense line currents, while direct 
connections monitor line-to-line voltages. An interface 
unit was designed to connect sensors and the recorder, 
which isolates and reduces voltage inputs, monitors correct 
phase rotation, shunts current transformer outputs to 
create voltage signals, and amplifies these line current 
signals as required for input to the recorder. Figure 13 
illustrates the on-board data collection arrangement. 

The complete system was tested by the Bureau, using a 
power system that simulated distributed capacitance 
grounding as would be the case for a submarine distribu- 
tion system. Use of the system and data collection 
procedures were completely documented, and the system 
was demonstrated for Navy personnel. 

SUMMARY 

Results for the failure prediction program, including 
monitoring system specifications, failure prediction con- 
cepts-theories, and mathematical models for power system 
component deterioration, have been delivered to the U.S. 
Navy. In addition, procedures and hardware for off-line 
power system performance-condition monitoring have been 
demonstrated and delivered. 



From power 
system bus 



Motor 

contactor 

case 



Current ' 
probes 



c£i 



c< 



-Voltage 
leads 



oA Va-B<> 

oB Vb-C & 

«C Vc-A°- 



IA » 
IB °" 
Ic °" 



Signal conditioning 
interface 



♦ * • 

To motor under test 



Instrumentation 
tape recorder 



Figure 13.— Equipment connections for submarine power 
system data acquisition. 



CONCLUSIONS 



The original goal of this program was improvement of 
the electrical component failure prediction algorithm 
developed by Penn State. Research scope was expanded, 
however, to further study the relationships between 
component deterioration and electrical terminal effects. 
The documentation of these relationships is the most 
important result of this research, since it forms much of 
the basis necessary for automated on-line failure 
prediction. An example from this theoretical basis is the 
predictable relationship between negative sequence current 
level and the power consumed in a deterioration leakage 
path. 

The analysis techniques and tools developed to study 
deteriorating electrical components are another significant 
result of this program. Mathematical models of cable- 
connected motor systems and deteriorating motors were 
developed to examine the effects of various deterioration 
conditions; but such models also have utility for electrical 
system design and maintenance and, as such, are valuable 
engineering tools independent of failure prediction 
research. 



With respect to the original program goal, results indi- 
cate that the existing failure prediction algorithm does not 
require modification, but the techniques used for imple- 
mentation must be revised to improve its performance. 
Monitoring sensitivity requirements should be thoroughly 
addressed in the application of the algorithm, in order to 
provide resolution adequate for low levels of deterioration. 
Any factor that introduces random fluctuations into 
measured values will adversely affect failure prediction 
accuracy. In addition, data acquisition techniques must 
provide reproducible sample points during motor opera- 
tion, to ensure valid comparisons between reference and 
test cases. A suitable method would trigger sampling 
based on motor load, but not be influenced by system 
deterioration. 

Beyond these accomplishments, research into electri- 
cally excited vibration in motor stators has confirmed the 
feasibility of modeling the connection between internal 
motor deterioration and stator vibration, and established 
the value of vibration as a parameter for use in detecting 
deterioration. 



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